When Italy placed a temporary ban on ChatGPT last year, it cited concerns over the protection of personal data.
Did this stop people from using it? No. Indeed, sales of VPNs rose by 400 percent after the announcement.
Nevertheless, it did underscore the rising concerns over the security and risk of large language models (LLMs).
In the enterprise, these expand far beyond personal data protection.
Indeed, Avivah Litan, VP Analyst at Gartner, noted five significant risks that generative AI poses for businesses today.
1. Fabricated Information
Litan suggests that “hallucinations and fabrications” - such as fabricated information passed off as fact – are prevalent problems with generative AI. She stated:
Training data can lead to biased, off-base or wrong responses, but these can be difficult to spot, particularly as solutions are increasingly believable and relied upon.
For this reason, keeping a human in the loop is critical. Salesforce recently doubled down on this point when discussing the possible contact center applications of Einstein GPT.
2. Deepfakes
Much of the fear circling generative AI lies in its ability for particular users to create content with malicious intent. This is not only a political problem but also an enterprise issue.
After all, people may use the technology to generate fake news and images to attack businesses – alongside personnel.
An example of the latter is an AI-generated image of The Pope wearing a white puffer jacket, which has done the rounds on social media.
Referring to this, Litan noted: "It provided a glimpse into a future where deepfakes create significant reputational, counterfeit, fraud, and political risks for individuals, organizations, and governments."
3. Data Privacy
Employees entering private data into LLMs has already become a significant issue. In just one of many examples, three employees at Samsung recently fed ChatGPT with “sensitive database source code and recorded meetings.”
Discussing the dangers of such actions, Litan stated:
These applications may indefinitely store information captured through user inputs and even use the information to train other models — further compromising confidentiality.
Moreover, a security breach – through a data extraction attack – could put that data in the hands of malicious actors.
As such, it’s no wonder Gartner recently warned against the dangers of contact center agents using ChatGPT for self-automation.
4. Copyright Issues
Vast amounts of internet data has trained LLMs. Most of this is copyright material.
Therefore, some outputs may violate intellectual property (IP) protections and copyright laws.
After making this point, Litan delved deeper. She added:
Without source references or transparency into how outputs are generated, the only way to mitigate this risk is for users to scrutinize outputs to ensure they don't infringe on copyright or IP rights.
Again, this underlines the need for human supervision to continually monitor and review content generated by applications such as ChatGPT.
